TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models.
Develop ML in the Browser
Use flexible and intuitive APIs to build models from scratch using the low-level
JavaScript linear algebra library or the high-level layers API.
Run Existing models
Use TensorFlow.js model converters to run pre-existing TensorFlow models right
in the browser.
Retrain Existing models
Retrain pre-existing ML models using sensor data connected to the browser, or
other client-side data.
This repository contains the logic and scripts that combine two packages:
- TensorFlow.js Core, a flexible low-level API, formerly known as deeplearn.js.
- TensorFlow.js Layers, a high-level API which implements functionality similar to Keras.
- TensorFlow.js Converter, tools to import a TensorFlow SavedModel to TensorFlow.js
If you care about bundle size, you can import those packages individually.
If you are looking for Node.js support, check out the TensorFlow.js Node repository.
Check out our examples repository and our tutorials.
Be sure to check out the gallery of all projects related to TensorFlow.js.
Be sure to also check out our models repository where we host pretrained models on NPM.
There are two main ways to get TensorFlow.js in your JavaScript project: via script tags or by installing it from NPM and using a build tool like Parcel, WebPack, or Rollup.
Add the following code to an HTML file:
<html>
<head>
<!-- Load TensorFlow.js -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
<!-- Place your code in the script tag below. You can also use an external .js file -->
<script>
// Notice there is no 'import' statement. 'tf' is available on the index-page
// because of the script tag above.
// Define a model for linear regression.
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
// Prepare the model for training: Specify the loss and the optimizer.
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
// Generate some synthetic data for training.
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
// Train the model using the data.
model.fit(xs, ys).then(() => {
// Use the model to do inference on a data point the model hasn't seen before:
// Open the browser devtools to see the output
model.predict(tf.tensor2d([5], [1, 1])).print();
});
</script>
</head>
<body>
</body>
</html>Open up that html file in your browser and the code should run!
Add TensorFlow.js to your project using yarn or npm. Note: Because
we use ES2017 syntax (such as import), this workflow assumes you are using a modern browser or a bundler/transpiler
to convert your code to something older browsers understand. See our
examples
to see how we use Parcel to build
our code. However you are free to use any build tool that you prefer.
import * as tf from '@tensorflow/tfjs';
// Define a model for linear regression.
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
// Prepare the model for training: Specify the loss and the optimizer.
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
// Generate some synthetic data for training.
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
// Train the model using the data.
model.fit(xs, ys).then(() => {
// Use the model to do inference on a data point the model hasn't seen before:
model.predict(tf.tensor2d([5], [1, 1])).print();
});See our tutorials, examples and documentation for more details.
We support porting pre-trained models from:
TensorFlow.js is a part of the TensorFlow ecosystem. For more info:
Thanks BrowserStack for providing testing support.